{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:37:07.123000",
     "start_time": "2016-09-12T22:37:07.114000"
    },
    "collapsed": true
   },
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将建立一个逻辑回归模型来预测一个学生是否被大学录取。假设你是一个大学系的管理员，你想根据两次考试的结果来决定每个申请人的录取机会。你有以前的申请人的历史数据，你可以用它作为逻辑回归的训练集。对于每一个培训例子，你有两个考试的申请人的分数和录取决定。为了做到这一点，我们将建立一个分类模型，根据考试成绩估计入学概率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:41:59.715000",
     "start_time": "2016-09-12T22:41:52.726000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#三大件\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:43:37.650000",
     "start_time": "2016-09-12T22:43:37.635000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exam 1</th>\n",
       "      <th>Exam 2</th>\n",
       "      <th>Admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.623660</td>\n",
       "      <td>78.024693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.286711</td>\n",
       "      <td>43.894998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.847409</td>\n",
       "      <td>72.902198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.182599</td>\n",
       "      <td>86.308552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.032736</td>\n",
       "      <td>75.344376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Exam 1     Exam 2  Admitted\n",
       "0  34.623660  78.024693         0\n",
       "1  30.286711  43.894998         0\n",
       "2  35.847409  72.902198         0\n",
       "3  60.182599  86.308552         1\n",
       "4  79.032736  75.344376         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "path = 'data' + os.sep + 'LogiReg_data.txt'\n",
    "pdData = pd.read_csv(path, header=None, names=['Exam 1', 'Exam 2', 'Admitted'])\n",
    "pdData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:41:59.785000",
     "start_time": "2016-09-12T22:41:59.776000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.260000",
     "start_time": "2016-09-12T22:41:59.790000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1bab4215208>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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z7PHHH9+n511SUmKe59nWrVub2nJycmzKlCktjnvggQds+PDh1qNHjxbl4/f2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\nkqQSWXRJkiRJUon+P5TpCxUICiZ0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab41ee080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive = pdData[pdData['Admitted'] == 1] # returns the subset of rows such Admitted = 1, i.e. the set of *positive* examples\n",
    "negative = pdData[pdData['Admitted'] == 0] # returns the subset of rows such Admitted = 0, i.e. the set of *negative* examples\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.scatter(positive['Exam 1'], positive['Exam 2'], s=30, c='b', marker='o', label='Admitted')\n",
    "ax.scatter(negative['Exam 1'], negative['Exam 2'], s=30, c='r', marker='x', label='Not Admitted')\n",
    "ax.legend()\n",
    "ax.set_xlabel('Exam 1 Score')\n",
    "ax.set_ylabel('Exam 2 Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目标：建立分类器（求解出三个参数 $\\theta_0         \\theta_1         \\theta_2 $）\n",
    "\n",
    "\n",
    "设定阈值，根据阈值判断录取结果\n",
    "\n",
    "### 要完成的模块\n",
    "-  `sigmoid` : 映射到概率的函数\n",
    "\n",
    "-  `model` : 返回预测结果值\n",
    "\n",
    "-  `cost` : 根据参数计算损失\n",
    "\n",
    "-  `gradient` : 计算每个参数的梯度方向\n",
    "\n",
    "-  `descent` : 进行参数更新\n",
    "\n",
    "-  `accuracy`: 计算精度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  `sigmoid` 函数\n",
    "\n",
    "$$\n",
    "g(z) = \\frac{1}{1+e^{-z}}   \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.268000",
     "start_time": "2016-09-12T22:42:00.263000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.574000",
     "start_time": "2016-09-12T22:42:00.270000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bab41d49b0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Fp58Err5USpJktbmiLYkSYV49lkYMya7pdcuu+SdRpIklSCLtiRJLbVqFQwe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab41d4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nums = np.arange(-10, 10, step=1) #creates a vector containing 20 equally spaced values from -10 to 10\n",
    "fig, ax = plt.subplots(figsize=(12,4))\n",
    "ax.plot(nums, sigmoid(nums), 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sigmoid\n",
    "* $g:\\mathbb{R} \\to [0,1]$\n",
    "* $g(0)=0.5$\n",
    "* $g(- \\infty)=0$\n",
    "* $g(+ \\infty)=1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.581000",
     "start_time": "2016-09-12T22:42:00.576000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def model(X, theta):\n",
    "    \n",
    "    return sigmoid(np.dot(X, theta.T))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{array}{ccc}\n",
    "\\begin{pmatrix}\\theta_{0} & \\theta_{1} & \\theta_{2}\\end{pmatrix} & \\times & \\begin{pmatrix}1\\\\\n",
    "x_{1}\\\\\n",
    "x_{2}\n",
    "\\end{pmatrix}\\end{array}=\\theta_{0}+\\theta_{1}x_{1}+\\theta_{2}x_{2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.649000",
     "start_time": "2016-09-12T22:42:00.586000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "pdData.insert(0, 'Ones', 1) # in a try / except structure so as not to return an error if the block si executed several times\n",
    "\n",
    "\n",
    "# set X (training data) and y (target variable)\n",
    "orig_data = pdData.as_matrix() # convert the Pandas representation of the data to an array useful for further computations\n",
    "cols = orig_data.shape[1]\n",
    "X = orig_data[:,0:cols-1]\n",
    "y = orig_data[:,cols-1:cols]\n",
    "\n",
    "# convert to numpy arrays and initalize the parameter array theta\n",
    "#X = np.matrix(X.values)\n",
    "#y = np.matrix(data.iloc[:,3:4].values) #np.array(y.values)\n",
    "theta = np.zeros([1, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        ,  34.62365962,  78.02469282],\n",
       "       [  1.        ,  30.28671077,  43.89499752],\n",
       "       [  1.        ,  35.84740877,  72.90219803],\n",
       "       [  1.        ,  60.18259939,  86.3085521 ],\n",
       "       [  1.        ,  79.03273605,  75.34437644]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.],\n",
       "       [ 0.],\n",
       "       [ 0.],\n",
       "       [ 1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.683000",
     "start_time": "2016-09-12T22:42:00.652000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 3), (100, 1), (1, 3))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape, theta.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 损失函数\n",
    "将对数似然函数去负号\n",
    "\n",
    "$$\n",
    "D(h_\\theta(x), y) = -y\\log(h_\\theta(x)) - (1-y)\\log(1-h_\\theta(x))\n",
    "$$\n",
    "求平均损失\n",
    "$$\n",
    "J(\\theta)=\\frac{1}{n}\\sum_{i=1}^{n} D(h_\\theta(x_i), y_i)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.712000",
     "start_time": "2016-09-12T22:42:00.686000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cost(X, y, theta):\n",
    "    left = np.multiply(-y, np.log(model(X, theta)))\n",
    "    right = np.multiply(1 - y, np.log(1 - model(X, theta)))\n",
    "    return np.sum(left - right) / (len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.743000",
     "start_time": "2016-09-12T22:42:00.714000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69314718055994529"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost(X, y, theta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算梯度\n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{\\partial J}{\\partial \\theta_j}=-\\frac{1}{m}\\sum_{i=1}^n (y_i - h_\\theta (x_i))x_{ij}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.769000",
     "start_time": "2016-09-12T22:42:00.747000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gradient(X, y, theta):\n",
    "    grad = np.zeros(theta.shape)\n",
    "    error = (model(X, theta)- y).ravel()\n",
    "    for j in range(len(theta.ravel())): #for each parmeter\n",
    "        term = np.multiply(error, X[:,j])\n",
    "        grad[0, j] = np.sum(term) / len(X)\n",
    "    \n",
    "    return grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient descent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较3中不同梯度下降方法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.830000",
     "start_time": "2016-09-12T22:42:00.804000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "STOP_ITER = 0\n",
    "STOP_COST = 1\n",
    "STOP_GRAD = 2\n",
    "\n",
    "def stopCriterion(type, value, threshold):\n",
    "    #设定三种不同的停止策略\n",
    "    if type == STOP_ITER:        return value > threshold\n",
    "    elif type == STOP_COST:      return abs(value[-1]-value[-2]) < threshold\n",
    "    elif type == STOP_GRAD:      return np.linalg.norm(value) < threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.858000",
     "start_time": "2016-09-12T22:42:00.833000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy.random\n",
    "#洗牌\n",
    "def shuffleData(data):\n",
    "    np.random.shuffle(data)\n",
    "    cols = data.shape[1]\n",
    "    X = data[:, 0:cols-1]\n",
    "    y = data[:, cols-1:]\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.918000",
     "start_time": "2016-09-12T22:42:00.862000"
    },
    "cell_style": "center",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def descent(data, theta, batchSize, stopType, thresh, alpha):\n",
    "    #梯度下降求解\n",
    "    \n",
    "    init_time = time.time()\n",
    "    i = 0 # 迭代次数\n",
    "    k = 0 # batch\n",
    "    X, y = shuffleData(data)\n",
    "    grad = np.zeros(theta.shape) # 计算的梯度\n",
    "    costs = [cost(X, y, theta)] # 损失值\n",
    "\n",
    "    \n",
    "    while True:\n",
    "        grad = gradient(X[k:k+batchSize], y[k:k+batchSize], theta)\n",
    "        k += batchSize #取batch数量个数据\n",
    "        if k >= n: \n",
    "            k = 0 \n",
    "            X, y = shuffleData(data) #重新洗牌\n",
    "        theta = theta - alpha*grad # 参数更新\n",
    "        costs.append(cost(X, y, theta)) # 计算新的损失\n",
    "        i += 1 \n",
    "\n",
    "        if stopType == STOP_ITER:       value = i\n",
    "        elif stopType == STOP_COST:     value = costs\n",
    "        elif stopType == STOP_GRAD:     value = grad\n",
    "        if stopCriterion(stopType, value, thresh): break\n",
    "    \n",
    "    return theta, i-1, costs, grad, time.time() - init_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:00.951000",
     "start_time": "2016-09-12T22:42:00.922000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def runExpe(data, theta, batchSize, stopType, thresh, alpha):\n",
    "    #import pdb; pdb.set_trace();\n",
    "    theta, iter, costs, grad, dur = descent(data, theta, batchSize, stopType, thresh, alpha)\n",
    "    name = \"Original\" if (data[:,1]>2).sum() > 1 else \"Scaled\"\n",
    "    name += \" data - learning rate: {} - \".format(alpha)\n",
    "    if batchSize==n: strDescType = \"Gradient\"\n",
    "    elif batchSize==1:  strDescType = \"Stochastic\"\n",
    "    else: strDescType = \"Mini-batch ({})\".format(batchSize)\n",
    "    name += strDescType + \" descent - Stop: \"\n",
    "    if stopType == STOP_ITER: strStop = \"{} iterations\".format(thresh)\n",
    "    elif stopType == STOP_COST: strStop = \"costs change < {}\".format(thresh)\n",
    "    else: strStop = \"gradient norm < {}\".format(thresh)\n",
    "    name += strStop\n",
    "    print (\"***{}\\nTheta: {} - Iter: {} - Last cost: {:03.2f} - Duration: {:03.2f}s\".format(\n",
    "        name, theta, iter, costs[-1], dur))\n",
    "    fig, ax = plt.subplots(figsize=(12,4))\n",
    "    ax.plot(np.arange(len(costs)), costs, 'r')\n",
    "    ax.set_xlabel('Iterations')\n",
    "    ax.set_ylabel('Cost')\n",
    "    ax.set_title(name.upper() + ' - Error vs. Iteration')\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同的停止策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 设定迭代次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:02.222000",
     "start_time": "2016-09-12T22:42:00.953000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 1e-06 - Gradient descent - Stop: 5000 iterations\n",
      "Theta: [[-0.00027127  0.00705232  0.00376711]] - Iter: 5000 - Last cost: 0.63 - Duration: 1.18s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.00027127,  0.00705232,  0.00376711]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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It9fMO54clPlCD9u5DxgfEa/tTyb7adWaz+RlNekOJx/nk4EjyS2GG44ndwH+CkVQsSij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\nkiRJklQn/j+h/Kri/oI3KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab5b1ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#选择的梯度下降方法是基于所有样本的\n",
    "n=100\n",
    "runExpe(orig_data, theta, n, STOP_ITER, thresh=5000, alpha=0.000001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据损失值停止"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定阈值 1E-6, 差不多需要110 000次迭代 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:23.816000",
     "start_time": "2016-09-12T22:42:02.225000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 0.001 - Gradient descent - Stop: costs change < 1e-06\n",
      "Theta: [[-5.13364014  0.04771429  0.04072397]] - Iter: 109901 - Last cost: 0.38 - Duration: 24.47s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-5.13364014,  0.04771429,  0.04072397]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ocQ6cbq0nSPwesJmZrVal9a1LBGzvzHtQTdxM/Ca7VLC+VZN/C03IuxI3vWeWWsgiGLkZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab46a69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(orig_data, theta, n, STOP_COST, thresh=0.000001, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据梯度变化停止"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定阈值 0.05,差不多需要40 000次迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:32.050000",
     "start_time": "2016-09-12T22:42:23.818000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 0.001 - Gradient descent - Stop: gradient norm < 0.05\n",
      "Theta: [[-2.37033409  0.02721692  0.01899456]] - Iter: 40045 - Last cost: 0.49 - Duration: 10.79s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-2.37033409,  0.02721692,  0.01899456]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9BOwV7XsHAe+FEKp1ThhCGBNCOCuE0B3/6k93oMZO1gstSo70T/DPDo8KITyX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab5b093c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(orig_data, theta, n, STOP_GRAD, thresh=0.05, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对比不同的梯度下降方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stochastic descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:32.786000",
     "start_time": "2016-09-12T22:42:32.052000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 0.001 - Stochastic descent - Stop: 5000 iterations\n",
      "Theta: [[-0.38504802  0.09357723 -0.01034717]] - Iter: 5000 - Last cost: 1.59 - Duration: 0.42s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.38504802,  0.09357723, -0.01034717]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XmoUNHUX9CJ3PX/U7p3wDaEd6uFdGBJ+z7hfmc+hYO+8N40noOr+dA77rBQAv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NddJ++d6Xno9cukodaFiSRhva1N+saOH1+/fb314svnyjnnyIFuOGjRyJHAoEFuWknf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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab472cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(orig_data, theta, 1, STOP_ITER, thresh=5000, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有点爆炸。。。很不稳定,再来试试把学习率调小一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:34.091000",
     "start_time": "2016-09-12T22:42:32.788000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 2e-06 - Stochastic descent - Stop: 15000 iterations\n",
      "Theta: [[-0.00202012  0.01009114  0.00103943]] - Iter: 15000 - Last cost: 0.63 - Duration: 1.10s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.00202012,  0.01009114,  0.00103943]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Ft/55AVm201tLz8hz9N7kNV027yOvIEerS+lQes83a4f48dy/LuTPPY0bcnS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\niIhIkVJQQERERERERKRI/T9dMwG+beSWtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab47a51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(orig_data, theta, 1, STOP_ITER, thresh=15000, alpha=0.000002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "速度快，但稳定性差，需要很小的学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mini-batch descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:36.331000",
     "start_time": "2016-09-12T22:42:34.094000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Original data - learning rate: 0.001 - Mini-batch (16) descent - Stop: 15000 iterations\n",
      "Theta: [[-1.0352224   0.01668297  0.0124234 ]] - Iter: 15000 - Last cost: 0.57 - Duration: 1.44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.0352224 ,  0.01668297,  0.0124234 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7mlfeh52IP0zXZbDPkT5AVCueYm7PpYpkRrSqKF7eg9fy10pXG93eg2Z2Cd76\nkix4p7mMuQUPSB95P818eO+Rih3x9OP7DH6Qfh3Y1Zofq+ROvGv5dmZ2SIczN3GP4ANzLRd85PtW\njMczNCsy8PWAFeD975jHVQbzepg2mH/l4jPApTVa6JoSPf/vxitelse7J1e62m8Umhu4rxW/wuNC\nU+9bNuBmBt+7rWT6K2lJ3dHw40IIz0fndXiNAt338fiSlt8pjCiPMy8D4/U90bTkgHzrxebHl01L\nq9fDB4ecGlvOomXHxva/OP7+dL0xnnITQnjVzH6Pj1H0tdg7xeviYT+Cwc+/RfA863eA09vc/0wz\nuw5/9WK1WLfxuK3xFv568e1XeHfvhnukhRDeMrPHqD3g3KsMviczz0s1YTJe0fmhRCv/mvD+51tb\nEkKYEVWmbmNmP8EbpO4IPnh1ctn/4GWHg8xsFJ5v3pTBr5G0FJQq0ycCi7VZsbgN8EwIYcCr2GaW\nNt5Coyrn/OPEKsiiNGBZOljpV+R3+AGOwWv0TjN/h/x90f9/xjNpx9bbUPBP3I3DR82tOhpp1Jp5\nBrCZme1VZbGWzluY+y7/Z0m812Q+evQQ/BuplyZ/8O4zG5q/N94RUWH/ZDwinhjrNbEjHqGPSQnb\nxXjGo5UxBkbg3yROO95j8Uz59tVWDiE8GUK4MfZzRwthSN10A8t8D68YSjsnF9D4aP1n4pmMRj5D\n9Wt8IK0DGgzjICGEa/GW04NJVPhFD/D/4gOnVRsdv92uwr/HM5Y7x6a1FQ+yYI1/lu9S5r4yVFl3\nHnwQlidCCPEEfGnzz0rFu29ejGeCdo4t9wH8HruxkmmOCof/wh+mS8eW3QovhF1IBkII7yTuoRvD\nwMFPG/EzvCKprQxdzK/xVvpR1InnIYTnEmFPVkgOEhUqVybKnEbdMLcHrg8hXJYSB/+AV+I1NVZJ\n8HE5jsALwyebDX6H0cy2jiqus3Q7cwsVrbog2kby8z+74b0obk9MHwbVP4nXCDNbAa88ngn8roX1\nP2pmq6RMXxTvzfEOPtgReC+6afgAVoM+JWhmK5tZJmNJRD0Nx+DxObMR+0MIL6fcu1XvFzPbwNLH\n/ai8M/pIyrx6fo2/gvE3S3y9Ieq59Uu84q7pL79YE5/lS5F6HszsA5b49FXkl9E68ddUxuCv3+2Z\nWPbHeE+WeEHiYmCtqCWzsq+P4i2a7/fCiApDdwG72cCxdPbA77fkKN1Fcxr+qlI8v1Lpzn90yjP8\nHDydzWpz0M6WAAAgAElEQVQMqt/i1+lMS7yDbz4myx/wXkhpjWbvCz7+zDV4XmrAGEFmtrb560Ak\npq+I9y54LDkvtt2ZKfdkuz0b2nElHq+Sg6xWnq3tFrgvwF8J3BX4PIlecJb+md17SbyuaGarm9nK\nLYbhbdJ7tF2If1rvm8kZ5p/rq/clF/A8fog/u83sswx+/3863mDTSM+6q/Dj3y8xff/od8cGKy90\nC3/wkbp3wjMB95vZ3/AWqVXwCLYUsH2IjcAbqVYoOQZPfHemduZ0P7zwfZKZbY/XFr6IR+wv4V3p\nWm3JqLzLvw4DH0oj8dapajfg5Xhitz0Du/tva2ZpXYzPDCnfF45Z3MwqifAi+CdxtsYHghjNwFGH\nRwL31Nje5XiGdp0QQr0WTsAzH/i3on+TNj+E8JSZPRjtu5n3xuvuGq9lTI7nMCOEcExsmQ2rJFYT\ngn9yZgdgXI1W58uBY8xszTBwFNQBou6AJ+PvN321VoElak29CK9Jbaf1q9LKn2Z7PNNzhfmI1zfi\nGePl8K8KfA4v9LbqX3gG8Kdm9hc8I97JeLBQLJ4nXRjmvlt3B97tsuYrRFGadBpwYFSQuBe/Huvg\nI03H/RxPxNcl6iIfQrgx6hp5QvSAexofHfnDKesfjqc1/4m6Hi6FdyW7A3/Qvs/Mtsa7MlbSvs+Z\n2S+ivy8KIVTNpLQraiHNrJU0+Gjrl+K16+228sav/7z4iLd74GltJc59DW+5Th1bJYTwuJk9Sgtj\nlYQQTjGzT0b7XD/qpVPpLv9NvOfUJk0dUX3j8YHsNibxVRozq9zDhmd014rFk6tDCOOjcN9gZpfj\nX2FYHG+h3hzv7bB/SqXQ1/FnROq3mVN8OrouhmeShuGvcsyLf9M9rQfL+ubdrpMeCv7u+Zp4unUN\nXgn9El45tiP+PP9FiHrVhBCeNbPt8EEIHzQftfo+PE/0BbxyJ5nOLVslLZkeQrgkZXrc4fg5XJK5\nXzTott8BK0T31kP4uV4Pr3B9hhYq7EII/zKz3+IjjN9jZv/AC8OfwSuH3gC2Ca19BaDyWb7kF1VS\nmdmBeMXc6ni82j7KnAP8LqpEXRP/pNZovOC2AD6OwVeBc0PslaTgXxg4CvhVlOG/BR/kbBPgh4lj\n+hteMXCJmR2L97TYB392JiuvDsS7eN9kZmfhDSz7AudFlUOdtmqVePxKCCE5LscAUSv3n/E81FCi\nHm/4q4rVxpe6HO/Vt1JofpyGAXn5EMI4M9sHzwvcb2Z/x9O6j+NfPJofHxG+kZ5YhzO34jKeR9sA\nz7v9G6/AfAPPF++Cp5mHNXkMjfhMlWsyKbTxmlwI4Vrz1xcOjSpExuO9Jr8NnForb9qg6/CB+Y7F\nC7zJCqsDzWwz/Dn0FJ7P2QN/Bo6NLXc/nkdqdmw08GMaZWaH4b0TX4ryJCfix3lJlC7diQ+K+Sk8\nb7EW9V+NvALP6//b/NXwIXiF30PRsQDv9yK7B2+geQB/re6hkDIAYvAxuU4Dfmj+KtwN+PNvV+DK\nMHCE/myFEAr/g79rdw7+UJqO3+D/AD6RsuxOeK3M0JR5hhc2HgMsmnYTcG+VZb+PR+iX8JreqXhC\n/QNggcTys/FW8cr/H42mjaoRxll4BmC+aB831TkPT+BjE4AnSrNr/KxfYzs3JZadhid4ZwFfSyz7\n2WiZw2psb+VomWNT5l0FPJoy/bTo+Jevsd0jo+1+LKN4dFSN8zUtWmbTOuf1QLxL12zgoBr7WiNa\n5ojYvmcBi6QsuwT+ULkyNu054IKUZdfCW8Bm4aP7V6b/KNrfJxLHW22ft0XLp+1jIbzS6za8O+90\n/N67DNi2wXOdGv5o3g+jfW8XxYPZrcQDatxj0fyL6lzLJWPLvkhKOlBlu/PgFWKT8VbDu4FvpSx3\nTHT+hyamL4y3MD6P107fAny5yr4+g38V4U08jfgrPvBhM8e6dRb3T7SfT0bb/GGd5S7Cx1aJT7sT\nuDUx7UU8o51cf+3o3M3CP6FXmb5XtP81Gghr2jl5Fa9B/2Jsub9F99QyNbZ1dLT+kMT01aLpP6kT\nls3xzMNU/FnyPF6g3Di2zLBoW9+vso2T8cEaG7lOR+BdEdO2US2e7JlYdkE8DZmCF2DuB3ZK2eZC\nePq1dwPhWiqxzxl4AfF/0b6GpKwzrEaYZ+Mti+Dp6P74M3sKnm69ij/vvlslPCvjA0o9jt/Lb0Rh\nGQUsFFvu/hr7fzG23AHRtJVT9nV5NO+GKmH5eNp1yPDe3RDvFfkAnkl/NzruP1Il/Y3iwGzg93W2\nvUl0X70UbfcJPI0bdE81Go9jcSX1OZKy/Js1rtEi0TLL4+OOPIH3VHkbLzDsVWO7+0bn6V38dbqd\nqyy3DJ4vfSWKR2OANWucrzuY2+vkSBJ5yjrn5JdV5g/KByTmv1HjHI1r5Brhlf/v4i2oX4/WrXX+\nhkbL/Dxl3qBnQmzed6mSl8Ur5S7GX5majj+LTyc9/Tgo2s4KKfOujOZdE5v2EbxR7hY8nX4Pf079\nm0T+OIN7ctka12M2XglUWfbheDgT23kHOK3KvAXxSqfJ0bl6DM/HWqPbqHMMf4nCOjZl3pfx5/DT\nUZx5Bm9QXCNl36nxILHcqfgnZ5P3xKV4mjYb/3R4Zd5C+OsbD0f7fxEfmPcAYP7ENTi4yj5H4a8H\nvI1XSmwVhSOZvxmKVyC9E23vyET8WyC2rEXTH4/F36OABRPbTL3meLmq7vlK/lQKvSIiIlICUTfo\nx4ERIYSOvj9tZj/AMyurhO58RUZERESakPs7/Ga2h5nda2bTop/barw/XHkPbU7iZ7b5yKkiIiJ9\nLfjXQU6hsbFBWhZ1df4p3mVahX0REZECyr2F38w2x7s7PI53c9gZHwRqneDvSieX3wB/r3gNYt92\nDSG82I3wioiIiIiIiPSC3Av8aczsFeCnIYS/p8yrFPiXCCG80fXAiYiIiIiIiPSA3Lv0x0WfYdke\nHzU++dmfAYviI8I+Z2bXmlkrIzuKiIiIiIiIlFYhPssXfa/1dnxExTeBb4fqn4t4Hh+J9C589Mnd\ngbFm9vnQ4CfhRERERERERMquEF36zWw+/PM4i+Pf4t0d+GqNQn9y/bHA5BDCTjWWWQr/5Nok/DMI\nIiIiIiIiIp20EDAE/9TeK93eeSFa+EMIs4Ano3/vNrPP498+/XGDmxgHfKnOMpsC57YWQhERERER\nEZGWjQTO6/ZOC1HgTzEP3l2/UevgXf1rmQRwzjnnsNZaa7UYLCmSUaNGcfzxx+cdDMmIrme56HqW\nj65pueh6louuZ7noepbLww8/zI477ghRebTbci/wm9mRwFXA08AH8ZqPDYBNovlHAStUuuub2b7A\nU8CDePeI3YENga/X2dV0gLXWWouhQ4dmfyDSdYsvvriuZYnoepaLrmf56JqWi65nueh6louuZ2nl\n8lp57gV+YBngLGB5YBpwH7BJCOHGaP5ywEqx5RcAjgNWAN6Jlt8ohPCfroVYREREREREpOByL/CH\nEH5QZ/4uif+PAY7paKBEREREREREetw8eQdARERERERERLKnAr/0rBEjRuQdBMmQrme56HqWj65p\nueh6louuZ7noekqWLISQdxi6wsyGAuPHjx+vQTBERERERESk4yZMmMCwYcMAhoUQJnR7/2rhFxER\nERERESkhFfhFRERERERESkgFfhEREREREZESUoFfREREREREpIRU4BcREREREREpIRX4RURERERE\nREpIBX4RERERERGRElKBX0RERERERKSEVOAXERERERERKSEV+EVERERERERKSAV+ERERERERkRJS\ngV9ERERERESkhFTgFxERERERESkhFfhFRERERERESkgFfhEREREREZESUoFfREREREREpIRU4BcR\nEREREREpIRX4RUREREREREpIBX4RERERERGRElKBX0RERERERKSEVOAXERERERERKSEV+EVERERE\nRERKSAV+ERERERERkRJSgV9ERERERESkhFTgFxERERERESkhFfhFRERERERESkgFfhEREREREZES\nUoFfREREREREpIRU4BcREREREREpIRX4RUREREREREpIBX4RERERERGRElKBX0RERERERKSEVOAX\nERERERERKSEV+EVERERERERKSAV+ERERERERkRJSgV9ERERERESkhFTgF5Hesthi8OUv5x0KERER\nEZHCU4FfRHrLm2/CrbfmHQoRERERkcLLvcBvZnuY2b1mNi36uc3MvlFnnf9nZuPNbLqZPWZmO3Ur\nvCIiIiIiIiK9IPcCPzAFOAgYCgwDbgT+ZWZrpS1sZkOAK4AbgM8AJwJ/NbOvdyOwIiIiIiIiIr1g\nvrwDEEIYk5h0iJn9GFgPeDhllR8DT4YQDoz+f9TMvgyMAq7rXEhFREREpC9dcgksvjhsvHHeIRER\naUruBf44M5sH2A5YBLi9ymLrAdcnpl0DHN/BoImIiIhIv9p2W/8dQr7hEBFpUiEK/Ga2Nl7AXwh4\nE/h2COGRKosvB0xNTJsKLGZmC4YQ3utcSEVERERERER6QyEK/MAj+Pv4iwPbAmeb2VdrFPpbNmrU\nKBZffPEB00aMGMGIESOy3pX0mzffhOOOg0MPhXmKMDyGiIiIiIh0y+jRoxk9evSAadOmTcspNM5C\nAbsmmdl1wBMhhB+nzLsZGB9C2D82bWfg+BDCEjW2ORQYP378eIYOHdqBUEvfO/hg+N3v/JNx66+f\nd2jKy8x/FzDtEhGRktKzR0RaNGHCBIYNGwYwLIQwodv7L2oz5DzAglXm3Q5slJi2CdXf+Rfpjlmz\n/LcyAyIiIiIiUgC5d+k3syOBq4CngQ8CI4EN8EI8ZnYUsEIIYadolT8De5nZ74Ez8ML/tsDwLgdd\nREREREREpLByL/ADywBnAcsD04D7gE1CCDdG85cDVqosHEKYZGab46Py7wM8A+wWQkiO3C8iIiIi\nIiLSt3Iv8IcQflBn/i4p0/4DDOtYoERERERERER6XFHf4RcRERERERGRNqjALyIiIiIiIlJCKvCL\nZE2j9IuIiIiISAGowC+Slco3ekVERERERApABX4RERERERGRElKBX0RERERERKSEVOAXERERSXrx\nRXjssbxDISIi0pb58g6AiIiISOGsuSa89poGYhURkZ6mFn4RERGRpNdeyzsEIiIibVOBX5qz775w\n9dV5h0JERERERETqUIFfmnPSSbDZZt3d5xtvwBZbwKuvdne/IiIiIiIiPUwFfim+f/0LxoyB0aPz\nDomIiIiIiEjPUIFfREREREREpIRU4BcREREREREpIRX4RUT6zVlnwU035R0KEREREemw+fIOgIiI\ndNnOO/tvfV9cREREpNTUwi8iIiIiIiJSQirwi2RNraYiIiIiIlIAKvCLiIiIiIiIlJAK/CJZMcs7\nBCIiIiJSNu+9B5Mn5x0K6VEq8IuIiPS7CRNgnXVg5sy8QyIiIkm77gpDhuQdCulRKvB30n33wbe/\nrXe6RUSk2H77W7j3XnjxxbxDIiIiSbfckncIpIfps3ydNGoU3HgjTJ8OCy+cd2h6nypORERERERE\nGqYWfik+vRsv7XrgAVUYiYiIiEjfUYFfRMptwgT41KfgggvyDomIiIiISFepwC+SNbUkF8vzz/vv\niRPzDYeIiIiISJepwC/FpwK0iIiIiIhI01Tgl97RK+/y90o4RURERESk1FTgFxEREaceVSIiIqWi\nAr+IiEi/U88kERGRUlKBX0RERERERKSEVOAX6ZaXX4aHH847FCIiIlIEF18MRxyRdyhEpOTmyzsA\nIqVT7R3YddeFyZP1jqyIiIjAd77jvw85JN9wiEip9WcLfwhw4IHw+ON5h0T6yeTJeYdARERERET6\nSH8W+AGOOQZ23rkz237tNbjqqs5su5+pZVxERERE+o0GVpU29G+Bv5O+9z0YPlwF1Kz0SiLXK+EU\nERERkd6hMoW0QQX+Tnj22bxDICJJeliKiIiISJ9Rgb+T1OIrkj/dhyIiIiLSp1Tgl+J59lm1xkp2\nFJdEREREpE+pwC/F8sor8JGPwAkn5B0SERFpx+zZ8Kc/waxZeYdERESkb+Ve4Dezg81snJm9YWZT\nzewyM1ujzjobmNmcxM9sM1umW+GWDnnjDf89bly+4WiHWpSLSV37Rbrrn/+EvfeGs87KOyQiIiJ9\nK/cCP/AV4GTgC8DGwPzAtWa2cJ31ArA6sFz0s3wI4cVOBlRyogK0iJRdCHD00f5Z17KYPn3gbxER\nEem6+fIOQAhhePx/M9sZeBEYBtxSZ/WXQghvdChoUjRqoRWRsnrySTjoIHj8cfjLX/ILhypYRURE\nSqUILfxJH8Jb71+ts5wB95jZc2Z2rZmt3/mgNUgF0/6m6y8izZozx3/PmJHP/pVuiYiIlFKhCvxm\nZsAJwC0hhIdqLPo88CNgG2BrYAow1szW6XwoRUREpHBmzYLddoOpU/MOiYiISGHk3qU/4RTgE8CX\nai0UQngMeCw26X9mthowCtip4b2p62JxpV0bXS8REanm7rvhjDNg0UXhxBPzDo2IiEghFKbAb2Z/\nBIYDXwkhPN/CJsZRp6IAYNSoUSy++OL+zyOPwJZbMmLECEaMGNHCLiVzad1K1dVUREREREQKbvTo\n0YwePXrAtGnTpuUUGleIAn9U2N8K2CCE8HSLm1kH7+pf0/HHH8/QddaBeeeFNdeEyy9vcXciVagn\ngoiIiIhkRY1fPSOtIXnChAkMGzYspxAVoMBvZqcAI4AtgbfNbNlo1rQQwvRomSOBFUMIO0X/7ws8\nBTwILATsDmwIfL3JnWdxCCIiItkoe4Xh6ad7hftuu+UdEhERkb6Qe4Ef2AMflX9sYvouwNnR38sD\nK8XmLQAcB6wAvAPcB2wUQvhPR0ParLJn3ER6ie5HKbK8K6C7dX/86Ef+WwV+EWnGhAmwwAKw9tp5\nh0Sk5+Re4A8h1P1SQAhhl8T/xwDHdCxQWVNBoz/knWGXdLouIvnSM1BE2lXpDt2v6Um/HrdkolCf\n5SsdFTRERKQX6HklIiJSSirwd4MyUiL5qVcrvuOOcOqp3QmLSD/Rs09ERCR3KvCLSH8791zYc8+8\nQyH9rozdNfM6pjKeSxGRMrnoIhg7Nu9Q9I3c3+EXEekKtTZKLyhjPO3WMZXx3ImIlNF22/lvVdB2\nhVr4RbKmxEtEWqX0Q0RERDKkAn8nqJUhW8oAl1MIcNllur4ioOeGiEi3PPmkp7kTJ+YdEpGu6O8C\nvwoaIvm5/HLYemu48MK8QyIiFXouikjZ3XST/9Y75NIn+rvAL71BLV/l9PrrA3+LSH7KkM6qskJE\nRGQQFfilmHo541aGjLOISK9SGiwiIvK+/izw93JhskheeAHmzMl2m8qoiYiIiIjMpfyxtKE/C/wV\nunla9+67sPzy8Lvf5R0Skcaook96QRnjabePqYznUEREpEX9V+C/7TY499y8Q9H7pk/337femm84\nikiZzWJRxZ70AsVTkWIIAfbay0dyl+6YMAHGjcs7FCKlNV/eAei6n/yke/tSwU9ERKQ7VGkiWXj1\nVTjlFJg0CcaMyTs0/WHYMP+tfLNIR/RfC383KeFqns6ZiPSjMqZ9KoCLiIjkTgX+bihjRk6kV+j+\nk16iQrKIiCQpLyNtUIG/k5Rxa14vn7NWw/7EE/Daa9mGRUQkb8qgioiI5E4F/m7o5UKsdN7qq8Pn\nPpd3KPKjQoFIuekZKCJFpPyH9AkV+KV3lDlhnjgx7xB0nwoBIoOVOZ0T6SW6F0WkJPq7wK/EvDeo\nYFhO3b7/FI+kyBQ/s6Nnu7RD96KIlEx/F/ibMWcOTJvW2LKVh4UyHf1J1705vZi5euqpvEMgIiIi\n7ejF/IdIC1Tgb9Svfw0f+lDeoehPKkBLkVx7Lay6Ktx6a94hERERERGpSQX+Rl1zTd4hKJZOF8LT\ntt9qTezVV8NNN7UXnqL4xS9UI523xx/335Mm5RoMkY7oRNqu13dERERyM1/eARAZoBMZtc02899l\n6Cnw17/mHQIRKSMVkkVEiktptLRBLfzSGiU81encFFMZKnxERERERJrQ3wX+ThXMVLAQKQ5VwPSu\nNdaAP/wh71BIq3TviYiI5K6/C/ytUGFeRKQ7Hn8cDjgg71AU26RJsMoq8OqreYdE+smMGXmHQERE\nGqQCf6OaaanQZ/lap3PWf3TNO+P11+HII3V+y+7cc73Qr69GSLfccw8suCCMG5d3SEREpAEq8HeS\nMtr9qZPXvUxdZMt0LEX0i1/4z4MP5h0S6QVlel6V6ViK6N57/ffdd+cbDhERaYgK/I1qJwOhzEfj\nVAiUrHXq/iv6fV3pclv0cEo2sr7Os2fDlVe2tw3FPREpMqVR0idU4O8kFV6zpYRZ56AIdF9LkWQV\nH5PbOfFE2HzzbFpxdc9IL9LzVoqkLPFx9dVh993zDkXfmS/vAOSqlZsnBGVeuq1XznevhLMoyvLw\nEslSUe6LqVP991tv5RuOZigNliwoHol0zhNP+I90lVr4G9XOA0APD5HqKvfHjTfCSy/lG5ZGFKVA\nJuWi54T0GqWF0uuU7kqf6O8C/+OPd2a7/ZCAZP2gf+stH2laaitz3NpoIxg+PO9QNK7M12LsWBgz\nJu9QSFmoYFguZU77RERKqKUCv5kdamaLpExf2MwObT9YXfLyy53dvjI5jfvGN/xb0tLfnn66c9tW\nJrVxG24IW2yRdyhEmqNnroiIyCCttvAfBiyaMn2RaJ6UXdaFp+Q3pHs549bLYReR3tSvX6NIo8o9\naUcvxnkRkRpaLfAbkJYifgZ4tfXg9AA9CDpLGTXplKzv3V5JC3olnP2u1euUdZpZCUcvp8WK892h\n8yzSPb2cJkvumirwm9lrZvYqXth/zMxejf1MA64DLuxEQHOnGy0/lUzFyy/DI4/kGxbpPVnfu0oL\nsnf88TB5ct6hKIa84lcn9qt7pZzKfl3Lfnwi0nea/Szffnjr/hl41/1psXkzgEkhhNszCluxqCY7\nf7/+tf/oWpSLrmdn9ULmdf/94fzz4Y478g5J/nQ/9K7XX4drr4Xttss7JJ2lOCoi0lOaKvCHEM4C\nMLOngFtDCLM6EirJzjvveKv40KF5h6Qxykj0j14oiEr3zOrzx4nuh973ox/BhRf6gJeLROMav/02\nLLoo3HILfOlL+YZPRET6Uqvv8L8JrFX5x8y2MrN/mtmRZrZANkGTTOyyCwwblncomtfLmd9eDruI\n9CYN2pd/2lv58k/8nFW+PPLXv3Y/PJ2W9/kWaVcvpW8ibWi1wH8asAaAma0KXAC8A3wHODqboBVU\nM4lDERKShx7KOwStKcK5k94WAlxxheKSlJsKXZIXpa0iIj2h1QL/GsA90d/fAW4OIewA7Axs08yG\nzOxgMxtnZm+Y2VQzu8zM1mhgvf9nZuPNbLqZPWZmOzV7EE1pJVNVeRiusAKsv3624SkrZV4lK1de\nCd/8JlxwQd4hEemcThe62kmT+7VAWPbjLvtzunJ8WV/HsWN926+/nu12RUTqaOezfJV1NwaujP6e\nAizd5La+ApwMfCHa1vzAtWa2cNWdmw0BrgBuwD8FeCLwVzP7epP77o7XX4fbSzaWYTczNL2Wuejk\nuem1c1FLp+PQq9EXQivdbLNS9sy89CelWyKdNXq0/9YXSfKnNEn6TLOj9FfcBRxiZtcDGwA/jqav\nAkxtZkMhhOHx/81sZ+BFYBhwS5XVfgw8GUI4MPr/UTP7MjAK/zSgVBS59UcEFIc6TRUUvaXZ69UL\n90+342Decb4XrolIP8s7jRDpslZb+PcDhgJ/BH4bQngimr4tcFubYfoQEIBXayyzHnB9Yto1wBfb\n3Hd9zSQSeuiLlE+v3Ne9Ek5xeV+vPPY/Y4bv9+qru79vEZG8091mqJJC2tBSC38I4T7gUymzfgbM\nbjUwZmbACcAtIYRao80tx+CeBFOBxcxswRDCe62GoXQ6nZgpAeounW8RSdOLo/RPm+a/Tz0VvvGN\nzu2nWzr17ndRlfU4y3pcItK3Wu3SD4CZDWPu5/keCiFMaDM8pwCfAPL9WO3668Nbb8F991VfZsgQ\nWGYZGDdu8LxeqjFsVT8cY7N0TqRIlGntLUW7Xr2YnhXtHMYVOWzN6sW40Yp+OU4RKb2WCvxmtgz+\nKb4NgMpwox8ys5uA7UMIL7WwzT8Cw4GvhBCer7P4C8CyiWnLAm/Ua90fBSwen7DllowYMYIRI0bM\nndbIAHuTJ9cfeKVMD/hu07lLV8YMiK51Z5UxzpRZq9eriNc5rzDltd+0tKyI10Vq6+WeGk88Ae+9\nB5/8ZN4hEelbo0ePZnRlkM7ItEqPtpy02sJ/MrAo8MkQwsMAZvYJ4CzgJGBEjXUHiQr7WwEbhBCe\nbmCV24HNEtM2iabXdDw++MD7Lr+8wVD2qF57YClz1D+yvtaPPgrrrguTJsFSSykuSX/o5cJJ1op4\nz5fxupTxmLqpk+dv9dU7vw/JRxHTN0k1qCEZmDBhAsOGDcspRK0P2vcNYM9KYR8geud+LwYXxGsy\ns1OAkcAOwNtmtmz0s1BsmSPN7KzYan8GVjWz35vZx81sT3zAwD+0eDyN61YiOn2619JK79GDNh/n\nneev4tx1l/+v6yAi3VQrQ67MuigOiEhOWi3wzwPMTJk+s4Vt7gEsBowFnov9bBdbZnlgpco/IYRJ\nwObAxsA9eE/93UIIyZH7s9PthHqxxWCVVbq7T5Ey6tS9qwoF6YRW41VW8VHxWupRwbU5U6bAu+/m\nHQpJo/RO+kSrXfpvBE40sxEhhOcAzGxFvMf8Dc1sKIRQt4IghLBLyrT/AO33jZg5E+afv+3NpLr1\n1tbXnTkTnq83lEEDeu3BnJb4KkGWIum1e0p6Q97xKu/9i5TVyivDFlvACivkHRIR6VOttvDvjbfK\nTzKziWY2EXgqmvaTrALXFeec07ltd7OgGgLMmdO9/XWaMp/pdF6kWaow6w9KG+ZSnJeiGTt27t+6\nV8njkB0AACAASURBVEWky1oq8IcQpuBj320OnBD9DA8hDA0hPJNh+Dpvxoy8Q5CNnXeGeecdPL0X\nv82c3L4ejnO1ct5nzfJzOGZM9uGR4srrvnnnHdhzT3j77Xz236+KXMgtcthEkjQQpoiUTFMFfjP7\nmpk9ZGaLBXddCOHkEMLJwJ1m9qCZbdqhsBZDUR8AZ5+dz36zLlSocJ+9yruDJ52Ubzi6pZuVUTLY\n6NFw6qk+iKJ0ntLM3tBIujFjhl/PSy7pfHiyoLRQepXSTekzzbbw7wf8JYTwRnJGCGEacBq91qVf\net9773nifcUVeYfE6UHSmKwzi8nz3ulWGl1nkdr6tUDYag+1N9/032eckW14sqa0rz39el8Uia6B\n9JlmC/yfAa6uMf9a4NOtBycHZb/p++HB/Prr/vsvf8k3HHmZONGv8513Dp63664wfHj3w9SMrOJo\ntXu57Pd4Nf163NKaTt4//fAcgt46zh/9yAeSk8FUSdw/dE2kTzRb4F+W9M/xVcwCPtx6cCRz6t7c\nfZ08J2kPp3vu8d//+c/geX//O9xyS+fCk4VOt/T3u7Kdjyef9GP63/98fIpuuvde3/crr3R3v3no\nxXiTd5h76Xl4+uka16WeavHp9tvTK9hFRAqq2QL/s8DaNeZ/GsjgW3JSeHlnrKR5RcuMdjoOqbKr\nnG6+2X9/8YswalR3933++f77gQe6u99uUJqenfi5LGM6UcZjasb668PnP593KEREGtZsgf9K4Ddm\ntlByhpktDBwOFORF6gY1m8np9wedSK/RwJLldf31eYcgf0UeUbzb90re56DWtShjupH3+e6UTtxT\nnThXs2b5V1GkP5T1fpOuaLbAfwSwJPCYmR1oZltFPwcBj0bzfpt1IAuhmYd1GR/s3dZKwtbviWG/\nH3+FzsNA3T4fRS6ASnc98kjeIehNRb93lMcphq22gg98IO9QiEgPmK+ZhUMIU81sfeBU4CigkuoH\n4BpgrxDC1GyD2GFFf7D2m1YyEv2e+ej346+oNkp/Rb/d64oXndNvcalVv/lN3iHoLfXu2RdegAUX\nhCWW6E54iigEmDQJVlkl75A0xyz7dOPKK7PdXj/qpbRcz3RpQ7Mt/IQQJocQhgNLA18A1gOWDiEM\nDyE8lXUAC2P27LxDIEVXlMT4ySfh8cfzDoVk7c03YYEFYPz4vENSWy9loJpVlHtcyqnevbP88jBk\nSFeC0pA87ofzzoNVV4WHH+7+votk8uS8QyAiPaTpAn9FCOG1EMKdIYRxIYTXsgxUId1+e94hyJcZ\n7LJL3qGQWiqZxdVWgzXWyDcsRdTrhbXHHoOZM+HMM/MOSWN6/XznpcwVJv2smeta69554432w9LL\nKgNmvvBCvuFoRVZp4l13FaviR0QKr+UCf9/KKzP23//6w+KpHDtRxAsazZyH117zT2g1ogyZ3SyP\nYdo0uOGG7LZXZpXzroKm9KJ2422R084ih63TypwelfW6Fv2aqXVfRJqkAn9SUbvL3nij/y7aIEiN\nPBi32so/odXqdov+8O2k738fNt64sWX7+TzF6XN8rlfCKe3J+r5XvMnGW2/B00+3tm7Rr0G/PGuK\nfh1ERBqkAn8yQV933c7ta9q0zm27yB59tPl14telnx+6tTKMc+bAP/7hv4vixRfhIx+BKVO6v+9+\nyYQ2ywxOOKF750ej9PemavGjl65jUdKAEGCjjeCjH21uvaKEvxf0UrzsV1Om5H+d7rmnfz6TKVKD\nCvzdTIxOP739beSdeHaaEuHa4ufnwgu9B8All/j/9eJGN+LODTfAs8/CpZe2vo3p0+Ezn4EHH8wu\nXFnqxTh61FF5h0B6TS/G8yIZN65z2956685tu+i6ES/Lns/qhpdfhpVXhhNPzC8Mjz4Kn/0snHrq\n4Hm6xtJnVOCveP11+OAH8w5Fdcp81ddvCfibb/rvt97KNxxZmzgR7rsP/vCHbLbX7/dOv90XvUrX\nKTtFOpedCMtll2W/zVYU6Tx3QpbPjizPVS880yo9WvN8TfbVV/33xInVl+mFcymSARX4K+6/v7GC\nU9kfcM1q5Hx0OkEtSoKdV8tDUY6/qMp2zzZ6PP0aL84+Gz72sc7uo5NxqozXrYzHlKbW6yyNnIMs\n41UI8O672W1PslO2Z5KIFJ4K/NKafsnANUMP8bmKdC6KFBbpvJ/9rHaLTjuK2J1YYyb0trfeGlww\nbyWeHXvswPWOPRYWWQRmz24vfJId5ZtEJCcq8Hczk5RHhkyZwO7r9kO9SC2+ytB0XrvnuPIqSKcp\nLjQn7/OlZ0X7ap3DavM++EFYY43qyw4ZAl/6Uv19H3/8wP+vuMJ/F2lQ116TdTf8rLane7U5Ol8i\nKvCX1j77tDdwWi8p2vfXu9Xdt9mWvV556KnFsrOOO647+ynj9SvjMTVr9mwvXM6YkXdIBsu6S3wW\n6zbyXHrmmerLTp4Mt93WelikeJrJq0ydCu+807mwdFqeaWZR8oT9bNYsuOqqvEMhqMDfmcQo60Tm\nxRdh7Fj/u9HwnnwybLNN7yZ4rVyXfs2M9+o17ras40evxjd18W1fGe+5Ro/piitg//2z+epMp+R1\nfdrZ75NPtje/ml5Np8QttxxsvPHAab2Q/vRCGEH3R6cdfzwMH+7jpEmuVOBvVjuJQ6sJ4Kabwk03\ntb7fTlhyyc5st5Vz1O6D5dJL4ZVX2ttGp9Q6tmRcLMuDK6vjqJy7rDMevZKR6Qd5xfmy3GutqLTs\nv/devuEom9VWa29+UifTKaWB3XX77c2vc+aZc0epz0M/p5Ey1wsv+O+yfU2qB6nA302tJoCTJmUa\njK4reuZgm21ghx3yDkXrin5+W9XucZU1w1G2Vzh6SVnvtU7rVlzU9Sm/Mrzy1Y2w77IL/PCHnd+P\niPQEFfiL9tC46qq5NWJZ6PTxZZ3Byuuzcy+/3Pl9tKLW9Wv2vBQtrldT9AxdUcOVhyOOyLdiJr7v\nOXPK0eqsUfp7X5GuRZHC0u+6XSHVyQFaJ0+GhRaCZ5+tvZwq4UQKQQX+ohk+HDbZJO9Q5K+Vh0Te\nGRs92IqhWjzot6793Qjfb3+bz37T7LabZ0DzTgda1ep5y+J4hwyBiy9ubvtFPs9FDlu3FD196gVF\nGQQyKe9re8klcNRRXsF64435hkV6g9Lk3M2XdwAkxfPPZ7/NEPJ/SHTCQw/BJz+Zdyjg7rs7875c\nM9esVxPUrN/ZL7teOc5ux8dzzunevnr1Xqtm8uTB06rFs16Jf0VR5nEmynYfVBT9uPIO37bbNr5s\n3mGtRulYd+g8F4Za+JtNjPJOvFrt7tlrGj3Of/yjs+Fo1NCh8Kc/+d/djiON7q+bcSGrc3DvvR7u\nKVMaX6dX43wn9eK1b1WvDlbWyHl78MHBgx9147WqTsk7rnRKWY+rol/S2H45zk7o9Lm76y44/PDO\n7kOkRFTgL+KDORmmIj90sj5/7RxrJ85TCMX9BnLyeIsQT7L8ykIIcOWV/veECfW3U8R7uSjyODdF\niI/VzJlT3E9/1jpva68N223X+TDUovusurRr14ufCKynm3GgLN3G84gHvdDLY+ZM/1Z7szbaCH71\nq/R5EyYMrBhNC2O9cF96KTz6aPPhytu778IzzzS3zsyZxR3HSjKjAn8vaCdBLULmrMgZ/4pq5+n0\n0+FLX/Iu+0XT7Gf5ihAXWvW3v3k8auYb8p2Od+2cz9tvhzvu6P5+yyZ+Lpq53vPOC/vu2/p+OxG3\nGh1gK5kW5TVoXxbnoJVtPPUUnHJK+/vuhCLem++9B2PHZre9u+7q7ldtDjnEC3cvvti9fXaKXl8b\nbJllfAyRLA0bBiNHNnaeqi2zzTbw6U9nG65u2HprWGml5tbZeWf48Ic7EhwpDhX4y5RwllWenyF7\n+mn/nef3bOOK1IKUJuuBiSrH9u9/++9GWgJ64csU668P663X/nakuilT4O23q8//+99b33Yn4thW\nW7W2XqfjexaD9mWZRm25Jey1V3bbK6Isr+lBB8GGG2b3DIt3o+5GBUflGVyGL3D0k0bjxuuv1x/p\nvxUPPdT+NmbMaH8b3dZKb5hKT0opNRX4u/kOf561/83s+5prvItP1s4/H8aPz367WShSobkRjSTq\nl1xSfd706V4D/sor2YUpb7VeDegF995bu0KjV+JoUcK58sqw+ebZbrOTx/bUU/671fjarfOe9/Wd\nPr07+2nlOrTb26KZc1vvPFS2NWmS/+7EM72bmu3R1ol9tqvb906W+7vmGhgzZvD0Ij9fixy2dhx5\nZN4hkB6kAn9ZE4SKZhP8J56Ab3wj/XNb7RoxAtZdN9ttFqHb9vXXt779e+6pPb/aqwQbbVT/2H/w\ng+rzbroJzjsv+66xWV6Pdr8sUbl2vXCPv/YarLMOHHZY9WWKchzPPANTp9ZfLu/33QFuvrnzYchb\np7v0513Ab0YvhTVNM9dw1VU7F448nHRSYz3Yev0a96pvfAO22KLx5Tt9nbLaflGeq834xS/yDkHz\nevE8l4wK/L0gnrB1ukdCpQtsJz4N2AlFSERGj2593csua2y5Rgqvxx0H77xTfxtJ//lPc+/Gd1o8\nvuf1nnC3VboO3n9//WWzHBixng9/GH72s4HTVloJlluute1lod6xdOIVoHvvhRVXnHt/3X8/XH11\n4+tLdxXhudCORu7XRp/RnTwXWaatxx+f3baKqJfi5O9+540KjSjq87Wsn6LuNboGhaECvxRTUUbQ\n7qXE6qWXmu/q9dBDsMEGvl4rI+V2W6++UlPP/PP776Jdg5dfhmOP7ew+euHTqKecAs89BxMn+v/7\n7Qebbdb9cHRDo2leM9ch67E9stzPmWf6mBpZKlJak9egjlnr1fDXGkek01o9Zwcf3Pi74L16XWSu\nWmnqlCk+/3//6154pCNU4O+EbhQSZ82Cffbp3GByeX3bPa8Cdq88tNLOTzzs8YGNGukKOW2a/z70\nUNhxx7nT11nHB3tqVZ7ns3Kcyd9Z6cSxzTuv/27kPduiVIZlLavr1EuVdOD3bDPpeLXPtmZ1jett\npxfObzNh3H13/2pGp+V1D/b65/l6Ib7VUnmO5lnwl3Jo56sDFddfP7hhodZ9/MAD/vu66+rvu2K/\n/eY2YvRC3qNPqMDfbKGgKAPF3H47nHwy/P73nQ9Pu9p9YNdaP+vu30lFTqyyPt4LLpj79733wtFH\nN7+NVsKU9Tnu1jXL8vz///buO96K4u4f+OcLiIp57AJq7F3iDyP2gg0lUWNsiaDm0Rh9NBaMDfSJ\nXR+jqNhiN3ZEsYOoqAQ7inSUpmJBqQrIpZc7vz/mbO7ec3f3bJnZdj/v1+u87rlnd2fm7Ozu2Zmd\nUvSbWi9Rv1PSfMvySebSpfEHRTvmGGCDDcymxySbg6Xl+fqaxPDhybbP8z5OO8+KfozMm5dd3G+9\n1fh33c+sWQ2zIZiWh/zLQxrSEvRdJ0wADjtMd/+06c4789dakVjgL92F4MADvT8vyvfMazpNF8i6\ndAnfRw7w3i953VdhxamtzlPB2Mb+L1qeZp0feSl8rLkm0LlzvDCjjgXgt8/Tbm2Vdd6bYuN7eM0O\nYeLpXBTVFWB5z6800zd0KHDddf7L41xXbrwx/PRmaV63unWrvU779sAWW9hPS9ryfsxHleS4ee45\noEMH/X769MbLbO6nsuVBgbHAr5Ruvhx3DmQbkpwg772XXdwmhU3H3XfbTYfD9A/0kCG6j1yU/d2/\nv/dUTGEHdcxL65QoTByPeUxT2vHaSrON423OHOCaa/I1PV2YMLPu41i0yiIgP783zYGp48N2ns2c\nCTz6aNP4TB7fhxwSPCNKHH//e+1pQMvYbamI1x0gX/vQzRkn5scfzbYOufNOc2FRIbHAD+jmy7b6\nwruZuDCmdXF96KF04klq4cKG90n2Td7HDpg6FTjxROCnn8KHndcfNEdZ+6IHefttPR1itaJ/ryiu\nvVY/YfNz4YV6HWf+cJOUAh5/vOF9VNXnVJ5mt0hbWY7ZPF0ny7JPo/jpJ+Cjjxq+e7duwOmnZ5sm\nW5pj/mapaPv744+BbbcFXn1Vz5Cz/vrmwm7B4l5zxyPAhqQXmSJdpAYNAt55x3y4edsHYccRCMOv\n20UQ92B8ceNNg+kp48rWT/6ww4CTT463bd7OibiuuUY/YfNjsxD94YfAaac1/mzFitoDEvnt+7BN\neG1Ke+yCrM+jrONPQ5rTkWY980nXrsB++zX8X1fXeLlft660jvf585s2gY7CKx/KdAxn1fUnabh5\n/D399lv994sv4ofht1+cgYGzksf93czkosAvIgeIyAAR+UFE6kXk6BrrH1hZz/1aJSJtrSd2nXXi\nb1umi7zbwQebCyvvT9qjbPvNN/r7TJvW+PM43S7SuljW16cTT5CyTCMV11/+orsZ+Ul7MLy8STJQ\n55IlTT+77jrg8MPjtSiIMzDRggXh1lNKD7IUZj2binT8mLx2mGgBkmbccb37bnpxeQlbuMnqONx5\nZ2DTTeNvX6TzJ45a3y8PM/YkXSdtNvZZVk/487h/m6lcFPgBrAVgDIBzAIQ90hWA7QC0r7w2VkrN\ntpO8AEV58tqzZzmnBbJl4EDdeiEOZz+//rr+GzSfrc0+6nHC/sc/kqUlKb8px8IIO5jZggX6szff\njJa2NCgFPPKI7mYUtE6ccJNs7ycPT69mz9ZxLl8eb/vvv9d/lywBDjoIeP75puuY+k7vvKMrjYPy\n1/H883qQpREjGn9u8nz3Evb46NkT+PRTM3FGUZYxObxkMdOMMzVr1vKUD24zZmSdgmKKm59PPRVu\nINM8VzTkUVZP+JkPuZGLAr9S6g2l1FVKqVcARLlKzFFKzXZeMSOPtdl/RGmeGzauJIUeP336AIsX\nN/y/667AqacmD7esjj4aOOqodOMUAb7+2lx4Ufr7O95+20zcJlpMfPIJcPHFweveeCPQrl208H/4\nQf/t1y962orizjsb8j/tG2nb88FXfz55spn4AP2086yzzIVXbfRo/XfKlNrrfvml/lurwGG7RUzQ\n8fO3vyUPI2sm05b1oKlxRG3VldfvYUNz+q558ac/Ab/9bbxtJ02K3sU0j3ls43rJPvzNXpGPAAEw\nRkSmi8ibIrJvJqkYPNh+HDYuSGPHAk88YT7cMnD6UYWV5OJcnbdJ529OGn9SSX+o3NtPmlR7/b//\nXT/hBbKdok0p4Pbbm/Y/TRJmXHPm6ILYiy82fJakGbxtpis483gDF0Wtee9N5t+SJek84fXLE68B\nLGvx+/7jxun+1jbsuGO4+cy95O18q5b1+WJ76tVZs8yGF5XX90m6zy+4INn2JuWh5Wjfvvr/nXby\n7mKa9TEels3K2yLOLkRGFbXAPwPAWQCOB3AcgGkA3hGRgI6vlqR9Yta6GcyjPN/Ae4V92GH243AE\nzQUcNoyw0/LV2jYvTKcp7JPisLzSN348cNFFwFVXxQszbrxe0hyHwUb3gChhJonf3eIpjfiiSuPc\n3GMPYN117cfjp1YLgccfB1ZfPVxYHTsCv/lN/LQsXaoHcPQyebLuwlBLHq+ntY7ZKNeLl1+O39Ut\nLJMVXH376jnm/VrOvfQScMkl8cOPytTx8d13ZsKxKc1r5YABTT8Tyef5GCQPlSdUWq2yTkAcSqkp\nANxtIj8WkW0AXAggsJ36hQAaDbv3r3+hO4DuphOZR0UcEC8t7n3jfuJVxKn+/Pz738DZZwP339/w\nWVqFa9PbxAnf+WujAsoZWX7pUnNhupf17w9stVW8sKPE1atXvIHoTDB1o2/jvMvyPLF5fnz+ebbx\n13LDDdHGZog7jgMArLkmsM8+eoo4k7LK6+pj1i/sKAX+Y4+Nn56k4uwbZ/yLmTO9r5/HHaf/3npr\n/HTlybhxWacgP4pw30ml1a9fP/Sr6jb6c8bjpRSywO9jOID9aq10O4Dd3B+cfnqyGt4kF5Xf/U7P\nt5nkqX2tG9GiPvF1pzvL6YWcAb1Mhml6mygeeKBxgT/t+N1sHWs2m0C7nXYa0L070Lq1f9wmDBum\n56Y+5RTzYVfr3Tt5GGlfQ8IWbJIwVVmUl+trGHHSumqVftqWZcEwKvf3HDYsnXjSZPoegZLr1ElX\nTq22mpnwVq4EWrXSrVyaiyJdS8Mq80O5Znad6d69O7p3b/woedSoUejUqVNGKSpuk34vu0I39c9e\n2JP21VfNhmdCmfoOxeVO748/mg3PJJsj/MeVZV47g/E5wqRl1Kjw4X/4oQ7TPX1bnIERg3jlx6JF\n+u+8eWbicGaPMMFmfscd5NSmPHZPyvKcq477/vv1U9MPP8wmPY4o+8RrqsasJMlLv21tj2Y+cCCw\ncGGyMNz8+vRXV7ql3dXPtCStUarddZe5sEyp1TW1evYRG8pWyLRxfQjLZBdIykQuCvwispaIdHT1\nwd+68v9mleX/EJHHXetfICJHi8g2ItJBRO4AcDCAf1pJoO3+UosWZTPCcjW//ouUjIkWG2kVfpJc\nnFes8J6m0mQagr7fQw95rxtUqI9S2+pMrzhxov86aRSAn3kGGDIk+naOKJUcWUg6FWMeKiFqyWr6\nNRF9/CQRNGaIU0FqsgCYF3kvPNQaq8TvmEsy5seCBXpGmx494ofRnNg6hkxXPNv24IN6/JAwXYrI\nviL8ZlJiuSjwA9gdwGgAIwEoALcBGAXg2sry9gA2c63furLOOADvANgFwKFKqXespG6LLawE+x+/\n+AVw883ey6ZOBebOtRs/oAszrVsDX3xhP64gm2xiNjy//sgTJwLvv197e+dideih0efjjXMRTXpx\njLq9yUEgd91VT6mTVFAaTj+99vbV+312vBk78eKLOqwoT9ZN/rh9/72exq36+/z0E9Cli7l4bMlD\nJWZe4wwzCGetyr9Zs3Q3j7BPCp3C+MCB4daP4quv7HalGDFC74/Jk8PlBW8ym7KxT5yxS0yMhG/7\nvBYx8/tEyU2dqv+aurf1OraVCj6myvrkOc8tSilTuSjwK6XeVUq1UEq1rHqdXln+Z6XUIa71b1FK\nbaeUWksptZFS6lCl1HsxI0+aeDPr+jWz3Wab+PFVmzbNf9o3p++ijQJ/nAuFiH4iNWdOsng++MB7\n3Z13Bjp3rh2me38782JPmdLwg5UHcY4JGxfvCRMa3odJU5x0J306GSX+V17Rf+NWGMThTtPWWwO7\n7ea/blnZHHckzLo2K1jjFFb99kfv3noU8k8/jZ6Or74CWraMvp1Xer77Dth2Wz0+iC1O5eyYMfbi\nqGbquuq0dshrJYSNsW+iSKOJvuOpp+zHEVZZClB5Pa6bK+YH+chFgb9UPvkEmD49vfii9M/bcUdg\nr73spsekrbcG2rYNv75fLW8SXj/KO+zQtCLGbdAgXaGQhahN/23PgRxG1oO8mQ57yRIzFRPVXWxG\nj04epml5vLmoNeOBSVEGFO3f32wByR12FErpgfVMTd/oNCcePz5+mvzkqVAUNy177BF9GxvHiQ0m\n0pmngYVXrQLOPbfpeDCmNMenrzbuy6ql1bVr7lzg22/NhxtWEVvN5fnYbGZY4E+q+gTce+9sLwhl\noVS8J23vvBNtwCivC2jSC9RddwX38zYtqE9tlG3jbG9DWoPL2LhZVQq4+mo9en+U1jJh0hK1S4kj\n7R9c53vXinflSuCFF5p+HmVU8bSP17hdYJ57DjjxRN1NxLSw+WvqOHCHk4cKw7TYarXkZnL/Fa1f\nt5/qFgA2z/mpU4F77wUuu8xcmEWpeMmCqX0TZ2DKOPusQwdgyy2jb+enTx/vMcJsj7OTx/s+so4F\n/jTFHYwsbphhlpfNwQcD++9vLrw40wPawAtydH5jNNjIx8GDG947N9rLlunCf5otftLmtS9vvNF/\nffeI6PfeC5xwgvl5z/P4tMKZf3fBAntx2bxGDBmiuzLVip/XqWRM7j+n0JD3PPEbs0Ip4Lbb/Jvh\nd+3aeN0sWhtQMrb3t+nfgpkzzYZ38cV6yt1qfftGC+e77/R33W47/3WSPBiiUmCBP80D38aTxzjp\n93qq5jB1UTjuOODkk+NvX0ufPsDll9de77TT7KWhDKrz2NQP5KWXNgzolIWhQ5v2Kfab3qmWoH3k\nvHeORZGG9efNA667DjjrrPDpTvuHuG9foFevdOKaMgVo06bh/wsu0H+dPs5pPe1JYsAAe2H78fs+\naVZAdumiuzJV8xsYK428zHqAWS82WlHYjttvH8+Zk+74JQ7397jkkob3QcdC587xx6QISkNRKhGa\n24MdILilEVCcQq2J2bGce/ovv7R3LKQ9oDQZxwJ/HPPnZ52C8LxOMmd6sbDrx/HSS8DTT4db95NP\nood/8cXATTc1/swr7V7NpYp44Qnb9SDMd/v+e//1Te6bsFNz2cgPvxvV+fMbnrLaatLvtywumzdz\np5yiB3+zofr71yqg8ca4sbBpdQaXBIArr8xuvuW0bb898F68sXqN+/OfgXvuST/erbaK1oUtirZt\ngXbtgtepNaOEDX6D85oYu6Mox36R2B4pX0SPj+KlOj932EFXwudVWudR2K5Yffr471sqHBb443BP\nQWPjpj5K0/8yzJ+5995mwnFG0a/l2GPDh5l2DX8ataibbZbsuBk0KLiVSFhZDGC03nrAnnsmC6/W\nPq5e/9VXs638sCXsdHBxhdkXaQ3WZFqSwkn18mXLGt4//HCyuMOsn/W0eO74k46XM2lSsu0djz0G\nnHde8Do2jstvvmnowua3z+P0b66Ow+8hQd4HF4wbtsk0+YVVhOtUGEmPL1vxepkyRXezyxNTlR5R\nhN13F1+sr21+cdTXhw/rwAPDrUfWsMCfF1EvXo8+2vC+ekTZRYvip8PGj1BaAzuFLfC7n4htu23y\neNP4QY/b/LzWenHSeNRRuu91XtnqS+zVzDPo2Ha/v+gi8+kJy9YNl4m5t4Hk50mUChnTY6WYPi9t\nirqfw6xv+9qetJn6woXAuHH+6znT0eaNuxWWKXGPwV12AQ491GyYYcIIkzdFam0JJDs/brvNnvgz\nHQAAIABJREFUzPm1alX2ranSuh7muVIlD78JSbVsqSsFqBBY4I/D9ijRYcJ0CvxKAb/8ZeNlS5ea\nT1OWbF60v/5a/437NDFInGPDL44iTseStx+0uN0XohQg/da1MWK2+8mubV5zvdvK33799F+/Y/Oc\nc+zEW00EuPVWoK7Of500jvG8n0dhl2WhWzegY8f04lu82H9ZlH3z2WfJ0+KIMuuFl7Ctk0w7//za\n69TVBbdqiVuxnaen0o4wrXfCaNUK+NvfzIQVhlK6UGh69qo8F+ajKPr3ePzx4OVF/34lwgJ/Fj74\nwO5gOElGlrddmZFlOkaMMBNOtahNvOOEZTov3nzTbHhl5M63mTMb+v4PHJgsLC9x8tdpapcGr9Yz\nb70VLQxTx7C7QjNqa46oN7qXXgqsvXbDKPtxOfkf9LQ5aljVsu6nfN99ekpSk5I2Fx471lxawjC5\nb++4A7jqquThZPU7Pnu27uZgavBWv8L7mWeaCd8dh6OshZUnnrAfh7Mv58/XfcHdFbVR9uu4cY1n\ndymbLFo7iOjXFVekEzflAgv8WfwYHnCAnj4ujqhNKPP2xCUsG6Nh77GH/7KwYyi4rVypp6uqJc08\nCBvXHXfYTUcSebzJ2nhj4JZbmn6eRlr94qivTx5GFNVN+M84w36cpk2YEG+7KNPpeak+L3/80T/M\n6oHQoozSb3raqKjmzg2/blr9/OOul8VvpzvOCy8Err/eXJhh98msWWbO3Wuv1QMZ+lVy9egBbLpp\n9AqsNJujF+X+KY/X2iBh9uv48fp7dexY+7fGRHyOuXPTqSgMm6aZM/U4GoC57qNO3DffHG27oLAo\n91jgz8pXX/kvM9GEOC4bc3Xm6cco6owAQfugTx89XdXEid7Ls+jDHxUv1v6cfXPffbXXqSVoZow4\n4QWxcawMHw60bx/+ewDJn5aF3RfV4c6ZEy2eMPGZPk/OOw/YZhuzYYblPN1Jsr0jzW4lXtKckSBp\nt6/584OvJXkxZkz0beLkw913A9On+4cR55xbudL786xbv5D3vg06bj76qOG9VxeXOGOMhFl2yCHA\nrrvWDtuUWt9j4431bBxZ47lReK2yTkDh5f0kyPqJT96YbAbtDKwU1Mc3qrSn1PJa/8UXk6UhD0ye\nl0HNlFetalrA9HqiNm+e/81oXCK6efuyZcA66zSN371eUlOm6L9+lVth5PVaOXhw/G2TPEn+8cf4\n8SZRqxtElOMlSTepuF3P8lSBXC1ovw4YoF9HH62fbJtW60m5UsD77wNvvAH83/81XQ7oee+TVOrF\nXcctSZcVU9e+LFtu1eI87U0SVprX4qzO17jfMa1uQM69udPSa5dd0rtfL8LDKDKOT/jjiHshifvE\nKsm68+aFC2fGjPBx1pLnfnBJfujiNqcy8RQpau14WF5hHH984//nztU1zKYH3Xn9dbPhZeGii/R8\n1WHcfXftdaIen507A+uuaz7c5sS59uVlAMuk/db9mJhR4Omnw4UxaVK01lROnMOHNx4vIk+/HV6U\nin4dW7HCXloc7kKhex8edBBw443+Ydx2GzB6tOmUBatuPp/kWpX2de7hh4Fp09KN0+tpr1LeA6um\nIWyXI9N5Y6OyyYQVK8KNW/HUU/qvMzXoZ5+ZqQQOc83MakBppYBnnokeNxnBAn/SC0LWN9KmTtx7\n7mkanu0m/bNmmRvQJwt56ZvvXmZjNPh339U3kCYv1ErVHo3dNhODLXoNfug3fV/cpuZB0rrJy2Lq\nybwX9hxxxwXw07+/bu5v66nds89GT5Pj5JObfuaVzgsvBPbeOzis6vSuXAnstRew3Xb+69QKI6qo\nlevXXw+sv37DZ88/DxxxRLTw0vjN69Ch4X2tlhTO8jjpStrNIWwY7vXiDIJba5tVq4BevcI/IHGc\neSZwzDHh17d5TQtbGVcUtp9C27p/a93afxpLW7L8rYxyznz4IdC9u720UCAW+PMiL6Pjp3nhaN8e\nuOyy9OID7AwEVGufpdn/vn//aGHYroWvtnBh+iNnpy3OjWn1tknYzFObY4jECTvrSgGlGheu/ERN\n59SpjeNwizKo2pw53l1JBg2Klp6k7ruvdprjPPn2G7jSRp9tpfSo+e4b3DiVeGkU+L2mBxQBWgTc\n8tkYKBfQ+23iRKBdu2izXaTZUnDECKB3b+Caa/T/XjOxTJ7svW3QVIxAtCej9fVmn77micnxoaKO\nQxIlPr9wP/wwfBjvvlt7HVv3WmFaXGR1vCxalE28BIAF/nTlZUC8sP0307gxeecd+3FEZepiePbZ\nZsIBdN9LP2ldvE0csyeeqAfEcR9bWbeSSSJKH+64g8KZeLpehBvCattum3UKGmTREsxERWLbtt7z\nmYeZXcSkMGM/2GqKalOcQmmU2TVMUio4fcuXRw8z7KBojzyip+nzGrG/elYKGyPkx60Acv+/447R\n4ow6BeeyZUDLlsCDD0bbLmtK6QrKTTdtPEtH0srhoIGtw4g7OKlfOvffv/a2rSwPi2b6+le9f6LM\nshI3DsoMC/xpMjVQUpJ4olhzTeDVV5PHX+QTPqj2PuyTXBP54TV/uI396hWmyXicm6BevZKHZbuy\nKG4fwST70MS+tlEoStKc1qa0CoB5K2hG8fbbybZP+0kUoAsQ7hHckxLRT3C9Pk/CZouXJOnwY+u3\nOGy406fr6frS2k9ZDWb3wQd6OrmXXgrfetO513j5Ze/lWbUkDKNfP523w4Y1XRbUqiMoze7zNU5l\nVNi8PPNMoGvX6OF7ifKQLM6xZnocJYfTFfSii+yE7yhyWaAEWOCPc9LZaH5veyBAtygn3dCh0cOv\n5m6emiQtNvmlwz01TNjBaYokzSbabm+95b8s7P48+OB4cdvmVzj2S9PNNwMbbui/jt/+8KuN/+67\n2mnMk6wGm4ri44+9P0+jIkcpYNSo+NtHTcPXX+sby7Sva+74ttnGeyT7JAW4226Ll66w4YcV9B1s\nX7dM92UOGgDQy1ln6WbzQWPNmBhbJapTT42/rVd6fvhB//UaUb+Mgn63nHsor3XCHnvOwHa2uMfi\nKVKLSYep5vpB06zGSe+RR9obpJQiY4E/KVMnbVChOE2mbziKcDMf1k03Nf6/yE/90lBXB/zrX97L\nxo+3F69fvlQ/OTFxY2miwFdrHAu/dP7v/3p/7jWwWlKvvRZ9m7D75qqrwofZs2f8eLxcd1249ZYs\niR8HkOxa8cgjQKdOTZsI33KLnbi7dgW23DLaNnHEbQlmo2ucKVkOQGryemWL8wQ0TvcN5/8oXaPC\nToXq1z8/jLQqKILiuf56c2FFlVXf8Ly1Nosq63PRS5L9dPXVjQc0BfR9g3vWlbRntKBGWOA3VfAY\nPRr45S/jbz99ergmymk3l08a/vPPZ58GtyTjEvg95TMp6x8mk/v6kkuAM86o/cR54cLGN2a29oGJ\np6TVojSljdtFICqvGnVbLZHSvmkJW8gN6+qrw62X5c2Z05e11pRNts6b6gH+sr5RDdPCLsnAmXHT\nkqWhQxueKgdJc7Ryr/DTPnacqVDjxhtmu0mT9G+Yl6iDQiaR9+4kXvJ+/2pTXvLb3domaAaXWvvy\nuuv0gKZB3S/OOCNa2sgoFvhNTWVy113hfnAdXifr11/rv0lGsk26jYkRvt3beI12G9VddyUPo5Yk\nI4Jn9aNiI964A8t5qavTf92VLF4DVvXsCfz5zw3/J51aMM8/8nEk7Yea5VPHMIO1FUGUkbajbB+G\nc/4EdYOxqdb1N+6TtjAFwaQDdyUR5mlxXgr8XbqEWy9olP40hdlvM2eai8+ddy+95L9e3Px0zzzz\nl7/4r5ekdUqt1gpe/efzysR5UysMpdI9P084Ib244nAfe86MAyKNn8AD/pVXjqVLgRkz/JdnNSgp\n1ZSTqz+FZvrmfdIk4PPPzYbp5nfDH+VCbPLJusmCvY0fkyj5+9RTydMRJr5+/eKHDzROn99AXM89\n1/A+6IYpbxYs8F8W53ixdSNk6roR5Ts9/DCw887JmsvmXdhWI3Hy1bnuOYN5eQ0651aEii6/a0mU\n/ZPV9/QqgOalwB8kyyfsSfhNeRjnO3Tu3PD+uOP81zMxps0jjzS8NzmTSq20LF0aPi7T8n4epHHc\nv/BCtPVNPuGP+v1GjPBPQ610/fGPwCab+C9ngT+3WOCPw9bFLUw/0eofqzgXMvc2O+0E/OpX4dYN\nY+rUxk9ro6Ql79J4OhLl2ErjiV9dHdC/f7xtne8yd67O57DdO4IK0VHjbi7cg0tWS2Nf3HBD0zmk\nncq+Wk3Rk8j6+nHDDfbCjjrYURGOeXcrOBOFq6wHUjU9aF/UqdxMSmOfeXW1SHMGk7Sl2aQ/K34F\nz+r+2qa/X5RZC8JUgg8bBkyY0HS99dYDfvvb6OmrxdY4Ckn99a/By2vdd1YX+It43pYUC/x5cu65\n0bfJ24/EpZcCTz5Ze728pTsOG/1En346+TRaJpmorXUKfn/4Q/KwwlBK9yXL2n77NbwP24c/Lvc4\nCTZ/YP2+w5VXAu+/n+x4sZXuL78Epkypvd6iRXbiB5KP0p8kjLT6s4eRhzTYlPT7HXKImXR48RvY\nL8xgeHFELdQnfcIddh0bTHajStp9yM+nnzZtvm3TgQc2/Uyk6eDHNkXJh333BTp0aPr5/PnAG2+Y\nS5MpJo51r/3Tty/Qp0/8Y5hP+HOLBf44+vSJt11aP0a14gnqo1OGgngcYW5Oqp/wh72RjrJPBw0C\nDjss/PpJhekHF1eWN/fnn197HVvHuq0btiBhBjKzrfoJvyOt7+1nu+2AHXaovd6++yZPjx8TT6/z\ncm0+9lgz4UT5Pu5K5DD9oG3vK6cPa17yJEit37Yzzkjera863BUrGt/4O/vJufcown6zza8lm619\ns+ee+lpoQ5jrvlMJf/nldtJQK/6wyl4pGUbfvk0/C7s/kwyMTVaxwB+Hu49WfX1DH8uiePxx/2Vl\nv9gVtQAbxGa6Tj893HpetbpxB/PK634OK0mhN27/+6xuoIMKE0kHG4yThiSCmtBneUw6cSfJ40GD\n9JOqoho9uuG97WNdBBgzJngdZ0advFbKuDlpuuYa/wGBe/Qwe4y3bg3ce2+0bZx0OoO9+gmTzp13\nBk45JVr87jREFWe8lupB9pI+OHAPHGhT3EF1vcYYsNESIqk8nsM2md6PfMKfWyzwm7DeetnFbfpk\nHTDAbviOvF1UldJNzQ45BPj+e7Nh57kAO2tW8HL3iMZBefbMM00/i9uUOG/HRhR+aV+1Knqf7DzK\n6lgOupktwvESZ7+ZLEwedRRw0knRtnnggXDrRTmuk4xSboNfvjijWNcS9zt06QK0bBlv26icND78\ncDrxecUf5fivNchn0D53lk2c6P2UMk/8zu833shm+kTHkCHBy9u2bfx/2HS5j/e0z/20W56Fkfag\nfVHOQa/BaPN8H0uhsMBvwvLl0U84Uzf/pi9aYW90TEn7IhK0vy6/XM9n7Mdv0L5a3yFOE6drrvEe\nQMZLkmPA1Nz0QVNJZv3Dmgf19Xanl8xLdyE3k+f2rrv6LwvTfSNrJgeki7t+0FRKXsJOi7b77tHC\n9TJgQPgxPvJQaXDSSXpQsjh9t0V0oaq+Hjj7bDvpyzuv/WSya5wpcVtmhekiOGFC/FZwSQWFde21\nwdtWP8E9+2w92xMQ/H1MV3CF2R9JB7W2yWR+usf/YsGcfLDAb0qeB5RJIuzFI2qhNsm+mD279jqT\nJ+vR4cOq1YQT8B91ttY++p//CZ8Ox7XXhu8r++qr0cOPoyg/JHlofh1XVk36v/km3nZhnraFWTcJ\nU5VWeRO1hYw7D72a7+ft/K0+HgYOtBd2tUWLks8eMXx4su2B8K0okshjl6Dm2s/X3WLuzTdrr+8u\nXPtNaRvX2WebuyaEuX9yHphssknD94pyj1ZE9fXhZt+KI6/liSxnGqFALPAXXV5u4l5/Xf9N4yK0\nzTa119lxR2CffZp+nlYteRHCj8ovPVOnBqc16g2njUJzGPffn/xG1EaehQnTa4CsKHr1ir6Nlzw2\nnYxj880b/5/0mDQxSn8tzjUYyLabWZCk+/HBBxsPMBf32NprL2CjjfyXn3de0wp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CAAAX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab48cc128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(orig_data, theta, 16, STOP_ITER, thresh=15000, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "浮动仍然比较大，我们来尝试下对数据进行标准化\n",
    "将数据按其属性(按列进行)减去其均值，然后除以其方差。最后得到的结果是，对每个属性/每列来说所有数据都聚集在0附近，方差值为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:40.439000",
     "start_time": "2016-09-12T22:42:36.333000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Scaled data - learning rate: 0.001 - Gradient descent - Stop: 5000 iterations\n",
      "Theta: [[ 0.3080807   0.86494967  0.77367651]] - Iter: 5000 - Last cost: 0.38 - Duration: 1.13s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.3080807 ,  0.86494967,  0.77367651]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aD3o0dXqUSOKtWDxdej+txhwqX/f9hcrNAgvJr3LOA48R9w1/Lhp+DPGAr6kf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\ng4FhwwwGJEmGAJIkSR1OBGyySS4HHpjrUoJnn20aDFx+OZx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78hGd5W0eA+aVtZkILE0p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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab5a89cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import preprocessing as pp\n",
    "\n",
    "scaled_data = orig_data.copy()\n",
    "scaled_data[:, 1:3] = pp.scale(orig_data[:, 1:3])\n",
    "\n",
    "runExpe(scaled_data, theta, n, STOP_ITER, thresh=5000, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "它好多了！原始数据，只能达到达到0.61，而我们得到了0.38个在这里！\n",
    "所以对数据做预处理是非常重要的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:52.663000",
     "start_time": "2016-09-12T22:42:40.442000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Scaled data - learning rate: 0.001 - Gradient descent - Stop: gradient norm < 0.02\n",
      "Theta: [[ 1.071  2.63   2.411]] - Iter: 59422 - Last cost: 0.22 - Duration: 12.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.071,  2.63 ,  2.411]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qRvNNvPfcM9T11uvjRURERKQCNN/Eu1UrOOIIlXqLiIiISEVovok3qLqJiIiI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\nRETK4P8Dgw+qsw0bMgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8e7f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(scaled_data, theta, n, STOP_GRAD, thresh=0.02, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多的迭代次数会使得损失下降的更多！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:58.954000",
     "start_time": "2016-09-12T22:42:52.665000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Scaled data - learning rate: 0.001 - Stochastic descent - Stop: gradient norm < 0.0004\n",
      "Theta: [[ 1.14848169  2.79268789  2.5667383 ]] - Iter: 72637 - Last cost: 0.22 - Duration: 7.05s\n"
     ]
    },
    {
     "data": {
      "image/png": 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vky9j3MC8JNBgDesyAxTF2Fdk8NeQVxQPA7cBS0XE4kWy4QJgw+iwQdZBqtW5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab488c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = runExpe(scaled_data, theta, 1, STOP_GRAD, thresh=0.002/5, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机梯度下降更快，但是我们需要迭代的次数也需要更多，所以还是用batch的比较合适！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:59.693000",
     "start_time": "2016-09-12T22:42:58.957000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***Scaled data - learning rate: 0.001 - Mini-batch (16) descent - Stop: gradient norm < 0.004\n",
      "Theta: [[ 1.17096801  2.83171736  2.61095087]] - Iter: 3940 - Last cost: 0.21 - Duration: 0.50s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.17096801,  2.83171736,  2.61095087]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RRz2qxXVkCiGMxQN6/fG3tTUeiSDhb8x/7Ker0RZTvjnAW+QPolbm0bhcul00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\nlWddHHccPPkkfP99saOVJElSOSYrJEm1W4sW0L8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03mHk5pvhooty4mLPPaF37zzzwhkX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\nUt3UoQMceSRMmADTpuVdRQ46CN54A7bZJm+L2rt33ib1qafg7beLHbF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bab5b78278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runExpe(scaled_data, theta, 16, STOP_GRAD, thresh=0.002*2, alpha=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:59.703000",
     "start_time": "2016-09-12T22:42:59.699000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#设定阈值\n",
    "def predict(X, theta):\n",
    "    return [1 if x >= 0.5 else 0 for x in model(X, theta)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-12T22:42:59.760000",
     "start_time": "2016-09-12T22:42:59.706000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 89%\n"
     ]
    }
   ],
   "source": [
    "scaled_X = scaled_data[:, :3]\n",
    "y = scaled_data[:, 3]\n",
    "predictions = predict(scaled_X, theta)\n",
    "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]\n",
    "accuracy = (sum(map(int, correct)) % len(correct))\n",
    "print ('accuracy = {0}%'.format(accuracy))"
   ]
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
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   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "691px",
   "left": "0px",
   "right": "893.9px",
   "top": "32px",
   "width": "322px"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}
